Remaining useful life (RUL) of lithium-ion batteries is an important indicator for battery health management, and accurate prediction can promote reliable battery system design, as well as safety and effectiveness of ...
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Remaining useful life (RUL) of lithium-ion batteries is an important indicator for battery health management, and accurate prediction can promote reliable battery system design, as well as safety and effectiveness of practical use. Therefore, we extract the health factor during charging and a multi-kernel support vector regression (MKSVR) RUL prediction model to achieve high accuracy estimation of the RUL of lithium-ion batteries. Firstly, based on the current, voltage, and temperature data during charging, seven characteristic parameters that can reflect the battery capacity decay are extracted, and then, three health factors (HF) that are highly correlated with the capacity decay are screened out using the Pearson coefficients. Secondly, the Gray wolf cuckoo search optimization (GWOCS) model is used to realize the intelligent optimization search of the kernel function parameter combinations of the multi-kernel support vector regression, and then the RUL prediction model of the multi-kernel support vector regression is established. Finally, the validation analysis is performed based on the NASA battery aging data set. The results show that the improved multi-kernel support vector regression has higher prediction accuracy compared with the single-kernelsupportvectorregression, and its RUL prediction errors are all less than 5 cycles, and the maximum root mean square error is all less than 0.028.
Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted wides...
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Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly.
Battery life prediction is of great significance to the safe operation,and reduces the maintenance *** paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life predicti...
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Battery life prediction is of great significance to the safe operation,and reduces the maintenance *** paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the *** feature extraction,eight features are obtained to fed into the life prediction *** hybrid framework combines variational mode decomposition,the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward,uneven distribution of high-dimensional feature space and the local escape ability,*** parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search *** algorithm can improve the local escape ability and convergence performance and find the global *** comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction *** with other algorithms,the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%.With the advance of the start point,the RUL prediction accuracy of the proposed algorithm does not change much.
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